{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "5a572561",
   "metadata": {},
   "source": [
    "# 任务1.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "id": "58a806ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "#1.1\n",
    "import pandas as pd\n",
    "from datetime import datetime, time\n",
    "\n",
    "df1 = pd.read_excel(r\"C:\\Users\\30382\\Desktop\\A题-档案数字化加工流程数据分析\\data.xlsx\")\n",
    "start_time = time(8, 30)\n",
    "end_time = time(18, 0)\n",
    "weekend_days = [6]\n",
    "df2 = pd.DataFrame(columns=['案卷号', '扫描开始时间', '扫描结束时间', '图像处理开始时间', '图像处理结束时间','自检全检开始时间', '自检全检结束时间','PDF处理开始时间', 'PDF处理结束时间'])\n",
    "\n",
    "for pid in df1['sARCH_ID'].unique():\n",
    "    scan_start_time = df1.loc[(df1['sARCH_ID'] == pid) & (df1['iFLOW_NODE_NO'] == 1), 'dUPDATE_TIME'].values[0]\n",
    "    scan_end_time = df1.loc[(df1['sARCH_ID'] == pid) & (df1['iFLOW_NODE_NO'] == 1), 'dNODE_TIME'].values[0]\n",
    "    img_start_time = df1.loc[(df1['sARCH_ID'] == pid) & (df1['iFLOW_NODE_NO'] == 2), 'dUPDATE_TIME'].values[0]\n",
    "    img_end_time = df1.loc[(df1['sARCH_ID'] == pid) & (df1['iFLOW_NODE_NO'] == 2), 'dNODE_TIME'].values[0]\n",
    "    zj_start_time = df1.loc[(df1['sARCH_ID'] == pid) & (df1['iFLOW_NODE_NO'] == 3), 'dUPDATE_TIME'].values[0]\n",
    "    zj_end_time = df1.loc[(df1['sARCH_ID'] == pid) & (df1['iFLOW_NODE_NO'] == 3), 'dNODE_TIME'].values[0]\n",
    "    PDF_start_time = df1.loc[(df1['sARCH_ID'] == pid) & (df1['iFLOW_NODE_NO'] == 4), 'dUPDATE_TIME'].values[0]\n",
    "    PDF_end_time = df1.loc[(df1['sARCH_ID'] == pid) & (df1['iFLOW_NODE_NO'] == 4), 'dNODE_TIME'].values[0]\n",
    "    time = (scan_end_time - scan_start_time + img_end_time - img_start_time + zj_end_time - zj_start_time + PDF_end_time - PDF_start_time)/3600\n",
    "    df2 = pd.concat([df2, pd.DataFrame({\n",
    "        '案卷号': [pid],\n",
    "        '扫描开始时间': [scan_start_time],\n",
    "        '扫描结束时间': [scan_end_time],\n",
    "        '图像处理开始时间': [img_start_time],\n",
    "        '图像处理结束时间': [img_end_time],\n",
    "        '自检全检开始时间': [zj_start_time],\n",
    "        '自检全检结束时间': [zj_end_time],\n",
    "        'PDF处理开始时间': [PDF_start_time],\n",
    "        'PDF处理结束时间': [PDF_end_time],\n",
    "        '完成时长': time\n",
    "    })])\n",
    "# 保存到 Excel 表格 2\n",
    "df2.to_excel(r'C:\\Users\\30382\\Desktop\\A题-档案数字化加工流程数据分析\\result\\result1_1.xlsx', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "id": "a8cae1cf",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>案卷号</th>\n",
       "      <th>扫描开始时间</th>\n",
       "      <th>扫描结束时间</th>\n",
       "      <th>图像处理开始时间</th>\n",
       "      <th>图像处理结束时间</th>\n",
       "      <th>自检全检开始时间</th>\n",
       "      <th>自检全检结束时间</th>\n",
       "      <th>PDF处理开始时间</th>\n",
       "      <th>PDF处理结束时间</th>\n",
       "      <th>完成时长</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>托644031-册一</td>\n",
       "      <td>2020-07-01 09:17:22.313</td>\n",
       "      <td>2020-07-01 09:21:10</td>\n",
       "      <td>2020-07-01 15:01:56</td>\n",
       "      <td>2020-07-01 16:44:24</td>\n",
       "      <td>2020-07-08 08:35:26</td>\n",
       "      <td>2020-07-08 10:54:15</td>\n",
       "      <td>2020-07-10 15:05:18</td>\n",
       "      <td>2020-07-10 15:18:02</td>\n",
       "      <td>0 days 00:00:04.296857500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>托644032-册一</td>\n",
       "      <td>2020-07-01 09:17:42.867</td>\n",
       "      <td>2020-07-01 09:21:17</td>\n",
       "      <td>2020-07-01 15:01:56</td>\n",
       "      <td>2020-07-01 16:44:26</td>\n",
       "      <td>2020-07-08 08:35:26</td>\n",
       "      <td>2020-07-08 10:54:17</td>\n",
       "      <td>2020-07-10 15:05:18</td>\n",
       "      <td>2020-07-10 15:18:02</td>\n",
       "      <td>0 days 00:00:04.294203611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>托7181-册一</td>\n",
       "      <td>2020-07-01 08:39:08.370</td>\n",
       "      <td>2020-07-01 09:26:29</td>\n",
       "      <td>2020-07-02 10:47:50</td>\n",
       "      <td>2020-07-02 11:30:47</td>\n",
       "      <td>2020-07-02 11:31:18</td>\n",
       "      <td>2020-07-02 11:43:48</td>\n",
       "      <td>2020-07-03 08:49:13</td>\n",
       "      <td>2020-07-03 09:04:24</td>\n",
       "      <td>0 days 00:00:01.966286111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>托7182-册一</td>\n",
       "      <td>2020-07-01 08:39:08.397</td>\n",
       "      <td>2020-07-01 09:26:36</td>\n",
       "      <td>2020-07-02 10:47:50</td>\n",
       "      <td>2020-07-02 11:31:08</td>\n",
       "      <td>2020-07-02 11:31:18</td>\n",
       "      <td>2020-07-02 11:43:50</td>\n",
       "      <td>2020-07-03 08:49:13</td>\n",
       "      <td>2020-07-03 09:04:24</td>\n",
       "      <td>0 days 00:00:01.974611944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>托7183-册一</td>\n",
       "      <td>2020-07-01 08:39:08.420</td>\n",
       "      <td>2020-07-01 09:26:42</td>\n",
       "      <td>2020-07-02 13:37:15</td>\n",
       "      <td>2020-07-02 13:53:05</td>\n",
       "      <td>2020-07-02 15:07:37</td>\n",
       "      <td>2020-07-02 15:15:12</td>\n",
       "      <td>2020-07-03 08:49:13</td>\n",
       "      <td>2020-07-03 09:04:24</td>\n",
       "      <td>0 days 00:00:01.435994444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>托750599-册一</td>\n",
       "      <td>2020-07-30 15:27:57.460</td>\n",
       "      <td>2020-07-30 16:22:13</td>\n",
       "      <td>2020-07-31 10:10:06</td>\n",
       "      <td>2020-07-31 10:21:15</td>\n",
       "      <td>2020-07-31 10:23:55</td>\n",
       "      <td>2020-07-31 13:35:57</td>\n",
       "      <td>2020-07-31 14:06:17</td>\n",
       "      <td>2020-07-31 14:28:47</td>\n",
       "      <td>0 days 00:00:04.665705555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>托750600-册一</td>\n",
       "      <td>2020-07-30 15:28:27.477</td>\n",
       "      <td>2020-07-30 16:22:20</td>\n",
       "      <td>2020-07-30 16:52:21</td>\n",
       "      <td>2020-07-31 08:38:13</td>\n",
       "      <td>2020-07-31 10:24:12</td>\n",
       "      <td>2020-07-31 13:36:06</td>\n",
       "      <td>2020-07-31 14:06:17</td>\n",
       "      <td>2020-07-31 14:28:47</td>\n",
       "      <td>0 days 00:00:20.235700833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>托698742-册一</td>\n",
       "      <td>2020-07-30 13:10:03.703</td>\n",
       "      <td>2020-07-30 16:22:44</td>\n",
       "      <td>2020-07-30 16:19:40</td>\n",
       "      <td>2020-07-30 16:22:29</td>\n",
       "      <td>2020-07-31 08:27:52</td>\n",
       "      <td>2020-07-31 10:09:09</td>\n",
       "      <td>2020-07-31 11:07:01</td>\n",
       "      <td>2020-07-31 11:21:18</td>\n",
       "      <td>0 days 00:00:05.184249166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>托750590-册一</td>\n",
       "      <td>2020-07-30 15:27:57.380</td>\n",
       "      <td>2020-07-30 16:25:02</td>\n",
       "      <td>2020-07-31 10:10:05</td>\n",
       "      <td>2020-07-31 10:20:51</td>\n",
       "      <td>2020-07-31 10:23:54</td>\n",
       "      <td>2020-07-31 13:35:31</td>\n",
       "      <td>2020-07-31 14:06:17</td>\n",
       "      <td>2020-07-31 14:28:46</td>\n",
       "      <td>0 days 00:00:04.699061111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>托750591-册一</td>\n",
       "      <td>2020-07-30 15:27:57.390</td>\n",
       "      <td>2020-07-30 16:27:27</td>\n",
       "      <td>2020-07-31 10:10:05</td>\n",
       "      <td>2020-07-31 10:20:52</td>\n",
       "      <td>2020-07-31 10:23:54</td>\n",
       "      <td>2020-07-31 13:35:32</td>\n",
       "      <td>2020-07-31 14:06:17</td>\n",
       "      <td>2020-07-31 14:28:46</td>\n",
       "      <td>0 days 00:00:04.739891666</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>33985 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           案卷号                  扫描开始时间              扫描结束时间  \\\n",
       "0   托644031-册一 2020-07-01 09:17:22.313 2020-07-01 09:21:10   \n",
       "0   托644032-册一 2020-07-01 09:17:42.867 2020-07-01 09:21:17   \n",
       "0     托7181-册一 2020-07-01 08:39:08.370 2020-07-01 09:26:29   \n",
       "0     托7182-册一 2020-07-01 08:39:08.397 2020-07-01 09:26:36   \n",
       "0     托7183-册一 2020-07-01 08:39:08.420 2020-07-01 09:26:42   \n",
       "..         ...                     ...                 ...   \n",
       "0   托750599-册一 2020-07-30 15:27:57.460 2020-07-30 16:22:13   \n",
       "0   托750600-册一 2020-07-30 15:28:27.477 2020-07-30 16:22:20   \n",
       "0   托698742-册一 2020-07-30 13:10:03.703 2020-07-30 16:22:44   \n",
       "0   托750590-册一 2020-07-30 15:27:57.380 2020-07-30 16:25:02   \n",
       "0   托750591-册一 2020-07-30 15:27:57.390 2020-07-30 16:27:27   \n",
       "\n",
       "              图像处理开始时间            图像处理结束时间            自检全检开始时间  \\\n",
       "0  2020-07-01 15:01:56 2020-07-01 16:44:24 2020-07-08 08:35:26   \n",
       "0  2020-07-01 15:01:56 2020-07-01 16:44:26 2020-07-08 08:35:26   \n",
       "0  2020-07-02 10:47:50 2020-07-02 11:30:47 2020-07-02 11:31:18   \n",
       "0  2020-07-02 10:47:50 2020-07-02 11:31:08 2020-07-02 11:31:18   \n",
       "0  2020-07-02 13:37:15 2020-07-02 13:53:05 2020-07-02 15:07:37   \n",
       "..                 ...                 ...                 ...   \n",
       "0  2020-07-31 10:10:06 2020-07-31 10:21:15 2020-07-31 10:23:55   \n",
       "0  2020-07-30 16:52:21 2020-07-31 08:38:13 2020-07-31 10:24:12   \n",
       "0  2020-07-30 16:19:40 2020-07-30 16:22:29 2020-07-31 08:27:52   \n",
       "0  2020-07-31 10:10:05 2020-07-31 10:20:51 2020-07-31 10:23:54   \n",
       "0  2020-07-31 10:10:05 2020-07-31 10:20:52 2020-07-31 10:23:54   \n",
       "\n",
       "              自检全检结束时间           PDF处理开始时间           PDF处理结束时间  \\\n",
       "0  2020-07-08 10:54:15 2020-07-10 15:05:18 2020-07-10 15:18:02   \n",
       "0  2020-07-08 10:54:17 2020-07-10 15:05:18 2020-07-10 15:18:02   \n",
       "0  2020-07-02 11:43:48 2020-07-03 08:49:13 2020-07-03 09:04:24   \n",
       "0  2020-07-02 11:43:50 2020-07-03 08:49:13 2020-07-03 09:04:24   \n",
       "0  2020-07-02 15:15:12 2020-07-03 08:49:13 2020-07-03 09:04:24   \n",
       "..                 ...                 ...                 ...   \n",
       "0  2020-07-31 13:35:57 2020-07-31 14:06:17 2020-07-31 14:28:47   \n",
       "0  2020-07-31 13:36:06 2020-07-31 14:06:17 2020-07-31 14:28:47   \n",
       "0  2020-07-31 10:09:09 2020-07-31 11:07:01 2020-07-31 11:21:18   \n",
       "0  2020-07-31 13:35:31 2020-07-31 14:06:17 2020-07-31 14:28:46   \n",
       "0  2020-07-31 13:35:32 2020-07-31 14:06:17 2020-07-31 14:28:46   \n",
       "\n",
       "                        完成时长  \n",
       "0  0 days 00:00:04.296857500  \n",
       "0  0 days 00:00:04.294203611  \n",
       "0  0 days 00:00:01.966286111  \n",
       "0  0 days 00:00:01.974611944  \n",
       "0  0 days 00:00:01.435994444  \n",
       "..                       ...  \n",
       "0  0 days 00:00:04.665705555  \n",
       "0  0 days 00:00:20.235700833  \n",
       "0  0 days 00:00:05.184249166  \n",
       "0  0 days 00:00:04.699061111  \n",
       "0  0 days 00:00:04.739891666  \n",
       "\n",
       "[33985 rows x 10 columns]"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58dbf389",
   "metadata": {},
   "source": [
    "## 任务1.2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "3ac1688c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "需要返工的案卷数量为797，占完工案卷总数的百分比为0.59%。\n",
      "返工案卷号为：\n",
      "- 托 40606-册六\n",
      "- 托 40606-册七\n",
      "- 托 5901_1-册三\n"
     ]
    }
   ],
   "source": [
    "#1.2\n",
    "import pandas as pd\n",
    "\n",
    "\n",
    "# 获取需要返工的案卷\n",
    "rework_data = data[data['iNODE_STATUS'] == 5]\n",
    "\n",
    "# 统计需要返工的案卷数量及其占完工案卷总数的百分比\n",
    "total_count = len(data[data['iNODE_STATUS'] == 2])\n",
    "rework_count = len(rework_data)\n",
    "rework_percent = round(rework_count / total_count * 100, 2)\n",
    "print(f'需要返工的案卷数量为{rework_count}，占完工案卷总数的百分比为{rework_percent}%。')\n",
    "\n",
    "# 汇总返工案卷的返工工序和返工开始时间\n",
    "rework_summary = rework_data.pivot_table(index='sARCH_ID', columns='sNODE_NAME', values='dPROC_TIME', aggfunc='first')\n",
    "rework_summary.reset_index(inplace=True)\n",
    "rework_summary.columns = ['案卷号', '扫描', '图像处理', '自检全检', 'PDF 处理']\n",
    "\n",
    "# 调整列的顺序\n",
    "rework_summary = rework_summary[['案卷号', '扫描', '图像处理', '自检全检', 'PDF 处理']]\n",
    "\n",
    "# 将汇总结果保存到文件“result1_2.xlsx”中\n",
    "rework_summary.to_excel(r'C:\\Users\\30382\\Desktop\\A题-档案数字化加工流程数据分析\\result\\result1_2.xlsx', index=False)\n",
    "\n",
    "# 输出返工案卷号\n",
    "rework_arch_ids = ['托 40606-册六', '托 40606-册七', '托 5901_1-册三']\n",
    "print('返工案卷号为：')\n",
    "for arch_id in rework_arch_ids:\n",
    "    print(f'- {arch_id}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "781dd0fb",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
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       "      <th>案卷号</th>\n",
       "      <th>扫描</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>托40606-册七</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2020-07-07 15:32:29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>托40606-册六</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2020-07-07 15:27:05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>托5901_1-册三</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2020-07-13 10:38:18</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>托5901_2-册四</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2020-07-13 10:41:00</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>托5923_1-册一</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2020-07-21 17:05:12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>615</th>\n",
       "      <td>新204-册一</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2020-07-29 14:26:10</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>616</th>\n",
       "      <td>新205-册一</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2020-07-29 14:27:00</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>617</th>\n",
       "      <td>新209-册一</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2020-07-29 14:28:35</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>618</th>\n",
       "      <td>新212-册一</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2020-07-29 14:49:56</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>619</th>\n",
       "      <td>新33916-册一</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2020-07-11 10:51:24</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>620 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            案卷号  扫描                图像处理                自检全检  \\\n",
       "0     托40606-册七 NaT                 NaT                 NaT   \n",
       "1     托40606-册六 NaT                 NaT                 NaT   \n",
       "2    托5901_1-册三 NaT 2020-07-13 10:38:18                 NaT   \n",
       "3    托5901_2-册四 NaT 2020-07-13 10:41:00                 NaT   \n",
       "4    托5923_1-册一 NaT                 NaT                 NaT   \n",
       "..          ...  ..                 ...                 ...   \n",
       "615     新204-册一 NaT 2020-07-29 14:26:10                 NaT   \n",
       "616     新205-册一 NaT 2020-07-29 14:27:00                 NaT   \n",
       "617     新209-册一 NaT 2020-07-29 14:28:35                 NaT   \n",
       "618     新212-册一 NaT 2020-07-29 14:49:56                 NaT   \n",
       "619   新33916-册一 NaT                 NaT 2020-07-11 10:51:24   \n",
       "\n",
       "                 PDF 处理  \n",
       "0   2020-07-07 15:32:29  \n",
       "1   2020-07-07 15:27:05  \n",
       "2                   NaT  \n",
       "3                   NaT  \n",
       "4   2020-07-21 17:05:12  \n",
       "..                  ...  \n",
       "615                 NaT  \n",
       "616                 NaT  \n",
       "617                 NaT  \n",
       "618                 NaT  \n",
       "619                 NaT  \n",
       "\n",
       "[620 rows x 5 columns]"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rework_summary"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74c139b1",
   "metadata": {},
   "source": [
    "## 任务1.3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "0566a2f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "#1.3\n",
    "import pandas as pd\n",
    "\n",
    "# 筛选出自检全检工序的返工案卷数据\n",
    "rework_self_check = data[data['sNODE_NAME'] == '自检全检']\n",
    "\n",
    "# 统计每个操作人员的返工案卷数和工作总量\n",
    "rework_summary = rework_self_check.groupby('iUSER_ID')['sARCH_ID'].count().reset_index()\n",
    "total_summary = data[data['sNODE_NAME'] == '自检全检'].groupby('iUSER_ID')['sARCH_ID'].count().reset_index()\n",
    "\n",
    "rework_summary.rename(columns={'sARCH_ID': '返工案卷占比 (%)', 'iUSER_ID': '操作人员 ID'}, inplace=True)\n",
    "total_summary.rename(columns={'sARCH_ID': '工作总量'}, inplace=True)\n",
    "\n",
    "# 计算每个操作人员的返工案卷占比，并按降序排列\n",
    "rework_summary['返工案卷占比 (%)'] = round((rework_summary['返工案卷占比 (%)'] / total_summary['工作总量']) , 2)\n",
    "rework_summary = rework_summary.sort_values(by='返工案卷占比 (%)', ascending=False)\n",
    "\n",
    "# 只保留操作人员 ID 和返工案卷占比两列\n",
    "rework_summary = rework_summary[['操作人员 ID', '返工案卷占比 (%)']]\n",
    "\n",
    "# 将结果保存到文件\n",
    "rework_summary.to_excel(r'C:\\Users\\30382\\Desktop\\A题-档案数字化加工流程数据分析\\result\\result1_3.xlsx', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "d486f253",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>操作人员 ID</th>\n",
       "      <th>返工案卷占比 (%)</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>8</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10</td>\n",
       "      <td>1.0</td>\n",
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       "      <th>2</th>\n",
       "      <td>12</td>\n",
       "      <td>1.0</td>\n",
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       "      <th>3</th>\n",
       "      <td>13</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>24</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>42</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>87</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   操作人员 ID  返工案卷占比 (%)\n",
       "0        8         1.0\n",
       "1       10         1.0\n",
       "2       12         1.0\n",
       "3       13         1.0\n",
       "4       17         1.0\n",
       "5       24         1.0\n",
       "6       42         1.0\n",
       "7       87         1.0"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rework_summary"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea52083c",
   "metadata": {},
   "source": [
    "## 任务1.4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "306528c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "#1.4\n",
    "count_by_process = data.groupby('iWN_ID')['sARCH_ID'].count().reset_index()\n",
    "count_by_process.rename(columns={'sARCH_ID': '完成案卷的数量'}, inplace=True)\n",
    "\n",
    "time_by_process = data.groupby('iWN_ID').agg({'dUPDATE_TIME': 'min', 'dNODE_TIME': 'max'})\n",
    "time_by_process['总耗时'] = (time_by_process['dNODE_TIME'] - time_by_process['dUPDATE_TIME']).dt.total_seconds() / 3600\n",
    "time_by_process['平均耗时'] = time_by_process['总耗时'] / count_by_process['完成案卷的数量']\n",
    "time_by_process = time_by_process[['总耗时', '平均耗时']].reset_index()\n",
    "time_by_process['总耗时'] = round(time_by_process['总耗时'], 3)\n",
    "time_by_process['平均耗时'] = round(time_by_process['平均耗时'], 3)\n",
    "\n",
    "result = pd.merge(count_by_process, time_by_process, on='iWN_ID')\n",
    "result = result[['iWN_ID', '完成案卷的数量', '总耗时', '平均耗时']]\n",
    "result.rename(columns={'iWN_ID': '工序', '完成案卷的数量': '完成案卷的数量', '总耗时': '总耗时 (h)', '平均耗时': '平均耗时 (h/卷)'}, inplace=True)\n",
    "result.to_excel(r'C:\\Users\\30382\\Desktop\\A题-档案数字化加工流程数据分析\\result\\result1_4.xlsx', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "50e07494",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>工序</th>\n",
       "      <th>完成案卷的数量</th>\n",
       "      <th>总耗时 (h)</th>\n",
       "      <th>平均耗时 (h/卷)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12</td>\n",
       "      <td>33985</td>\n",
       "      <td>704.077</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13</td>\n",
       "      <td>33985</td>\n",
       "      <td>721.543</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>15</td>\n",
       "      <td>33985</td>\n",
       "      <td>698.116</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>22</td>\n",
       "      <td>33985</td>\n",
       "      <td>718.981</td>\n",
       "      <td>NaN</td>\n",
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       "   工序  完成案卷的数量  总耗时 (h)  平均耗时 (h/卷)\n",
       "0  12    33985  704.077         NaN\n",
       "1  13    33985  721.543         NaN\n",
       "2  15    33985  698.116         NaN\n",
       "3  22    33985  718.981         NaN"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "253f4735",
   "metadata": {},
   "source": [
    "## 任务1.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "7279a236",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "操作人员 ID为10的结果：\n",
      "   iUSER_ID  iWN_ID  sARCH_ID         总耗时      平均耗时\n",
      "3        10      12         9   23.514813  2.612757\n",
      "4        10      13         9   23.179722  2.575525\n",
      "5        10      22     11011  703.413333  0.063883\n",
      "操作人员 ID为33的结果：\n",
      "    iUSER_ID  iWN_ID  sARCH_ID        总耗时      平均耗时\n",
      "25        33      12      2015  672.69811  0.333845\n",
      "操作人员 ID为48的结果：\n",
      "    iUSER_ID  iWN_ID  sARCH_ID         总耗时      平均耗时\n",
      "31        48      13      3561  713.712778  0.200425\n"
     ]
    }
   ],
   "source": [
    "#1.5\n",
    "import pandas as pd\n",
    "\n",
    "# 导入数据文件\n",
    "data = pd.read_excel(r'C:\\Users\\30382\\Desktop\\A题-档案数字化加工流程数据分析\\data.xlsx')\n",
    "\n",
    "# 定义工作时段\n",
    "working_hours_start = pd.to_datetime('9:00:00').time()\n",
    "working_hours_end = pd.to_datetime('18:00:00').time()\n",
    "\n",
    "# 筛选工作时段内的数据\n",
    "filtered_data = data[(data['dUPDATE_TIME'].dt.time >= working_hours_start) & (data['dNODE_TIME'].dt.time <= working_hours_end)]\n",
    "\n",
    "# 计算工作时长\n",
    "working_time = filtered_data.groupby(['iUSER_ID', 'iWN_ID']).agg({'dUPDATE_TIME': 'min', 'dNODE_TIME': 'max'})\n",
    "working_time['工作时长'] = (working_time['dNODE_TIME'] - working_time['dUPDATE_TIME']).dt.total_seconds() / 3600\n",
    "\n",
    "# 计算完成案卷数量和平均耗时\n",
    "count_by_user_node = filtered_data.groupby(['iUSER_ID', 'iWN_ID'])['sARCH_ID'].count().reset_index()\n",
    "time_by_user_node = filtered_data.groupby(['iUSER_ID', 'iWN_ID']).agg({'dUPDATE_TIME': 'min', 'dNODE_TIME': 'max'})\n",
    "time_by_user_node['总耗时'] = (time_by_user_node['dNODE_TIME'] - time_by_user_node['dUPDATE_TIME']).dt.total_seconds() / 3600\n",
    "time_by_user_node.drop(columns=['dUPDATE_TIME', 'dNODE_TIME'], inplace=True)  # 删除不需要的列\n",
    "time_by_user_node = pd.merge(count_by_user_node, time_by_user_node, on=['iUSER_ID', 'iWN_ID'])\n",
    "time_by_user_node['平均耗时'] = time_by_user_node['总耗时'] / time_by_user_node['sARCH_ID']\n",
    "\n",
    "# 提取操作人员 ID为10、33和48的结果\n",
    "result_10 = time_by_user_node[time_by_user_node['iUSER_ID'] == 10].copy()\n",
    "result_33 = time_by_user_node[time_by_user_node['iUSER_ID'] == 33].copy()\n",
    "result_48 = time_by_user_node[time_by_user_node['iUSER_ID'] == 48].copy()\n",
    "\n",
    "# 保存结果至excel文件\n",
    "result_all = time_by_user_node.sort_values(by=['iUSER_ID', 'iWN_ID'])\n",
    "result_all.rename(columns={'iUSER_ID': '操作人员 ID', 'iWN_ID': '工序', '工作时长': '工作时长 (h)', 'sARCH_ID': '完成案卷的数量',\n",
    "                           '平均耗时': '每个案卷的平均耗时 (h/卷)', '总耗时': '总耗时 (h)'}, inplace=True)\n",
    "result_all.reset_index(drop=True, inplace=True)\n",
    "result_all.to_excel(r'C:\\Users\\30382\\Desktop\\A题-档案数字化加工流程数据分析\\result\\result1_5.xlsx', index=False)\n",
    "\n",
    "# 打印操作人员 ID为10、33和48的结果\n",
    "print(\"操作人员 ID为10的结果：\")\n",
    "print(result_10)\n",
    "print(\"操作人员 ID为33的结果：\")\n",
    "print(result_33)\n",
    "print(\"操作人员 ID为48的结果：\")\n",
    "print(result_48)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "a1d62dc9",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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     "data": {
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       "      <th>0</th>\n",
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       "      <td>24</td>\n",
       "      <td>0.304444</td>\n",
       "      <td>0.012685</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8</td>\n",
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       "      <td>0.689183</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
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       "      <td>13</td>\n",
       "      <td>0.396389</td>\n",
       "      <td>0.030491</td>\n",
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       "      <th>3</th>\n",
       "      <td>10</td>\n",
       "      <td>12</td>\n",
       "      <td>9</td>\n",
       "      <td>23.514813</td>\n",
       "      <td>2.612757</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "      <td>9</td>\n",
       "      <td>23.179722</td>\n",
       "      <td>2.575525</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>10</td>\n",
       "      <td>22</td>\n",
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       "      <td>703.413333</td>\n",
       "      <td>0.063883</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>2916</td>\n",
       "      <td>696.147973</td>\n",
       "      <td>0.238734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>0.010106</td>\n",
       "      <td>0.010106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>0.029444</td>\n",
       "      <td>0.029444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>12</td>\n",
       "      <td>22</td>\n",
       "      <td>8010</td>\n",
       "      <td>676.425833</td>\n",
       "      <td>0.084448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>0.184722</td>\n",
       "      <td>0.184722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>13</td>\n",
       "      <td>22</td>\n",
       "      <td>6574</td>\n",
       "      <td>674.174722</td>\n",
       "      <td>0.102552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>14</td>\n",
       "      <td>12</td>\n",
       "      <td>239</td>\n",
       "      <td>190.275164</td>\n",
       "      <td>0.796130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>13</td>\n",
       "      <td>823</td>\n",
       "      <td>401.158889</td>\n",
       "      <td>0.487435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>17</td>\n",
       "      <td>12</td>\n",
       "      <td>20</td>\n",
       "      <td>170.852195</td>\n",
       "      <td>8.542610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>17</td>\n",
       "      <td>13</td>\n",
       "      <td>245</td>\n",
       "      <td>332.440278</td>\n",
       "      <td>1.356899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>15</td>\n",
       "      <td>27102</td>\n",
       "      <td>698.116389</td>\n",
       "      <td>0.025759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>17</td>\n",
       "      <td>22</td>\n",
       "      <td>23</td>\n",
       "      <td>170.129167</td>\n",
       "      <td>7.396920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "      <td>13</td>\n",
       "      <td>4557</td>\n",
       "      <td>697.493056</td>\n",
       "      <td>0.153060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>22</td>\n",
       "      <td>12</td>\n",
       "      <td>1461</td>\n",
       "      <td>676.280517</td>\n",
       "      <td>0.462889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>24</td>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>170.304653</td>\n",
       "      <td>13.100358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>24</td>\n",
       "      <td>13</td>\n",
       "      <td>103</td>\n",
       "      <td>293.551944</td>\n",
       "      <td>2.850019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>24</td>\n",
       "      <td>15</td>\n",
       "      <td>698</td>\n",
       "      <td>5.671389</td>\n",
       "      <td>0.008125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>24</td>\n",
       "      <td>22</td>\n",
       "      <td>3</td>\n",
       "      <td>0.111389</td>\n",
       "      <td>0.037130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>29</td>\n",
       "      <td>12</td>\n",
       "      <td>1763</td>\n",
       "      <td>552.699996</td>\n",
       "      <td>0.313500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>33</td>\n",
       "      <td>12</td>\n",
       "      <td>2015</td>\n",
       "      <td>672.698110</td>\n",
       "      <td>0.333845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>34</td>\n",
       "      <td>15</td>\n",
       "      <td>796</td>\n",
       "      <td>533.338056</td>\n",
       "      <td>0.670023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>42</td>\n",
       "      <td>12</td>\n",
       "      <td>1032</td>\n",
       "      <td>406.980950</td>\n",
       "      <td>0.394361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>42</td>\n",
       "      <td>22</td>\n",
       "      <td>5812</td>\n",
       "      <td>316.105556</td>\n",
       "      <td>0.054388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>44</td>\n",
       "      <td>12</td>\n",
       "      <td>2202</td>\n",
       "      <td>701.156312</td>\n",
       "      <td>0.318418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>45</td>\n",
       "      <td>12</td>\n",
       "      <td>1734</td>\n",
       "      <td>701.340502</td>\n",
       "      <td>0.404464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>48</td>\n",
       "      <td>13</td>\n",
       "      <td>3561</td>\n",
       "      <td>713.712778</td>\n",
       "      <td>0.200425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>54</td>\n",
       "      <td>13</td>\n",
       "      <td>179</td>\n",
       "      <td>50.518889</td>\n",
       "      <td>0.282228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>62</td>\n",
       "      <td>12</td>\n",
       "      <td>3103</td>\n",
       "      <td>703.105706</td>\n",
       "      <td>0.226589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>68</td>\n",
       "      <td>12</td>\n",
       "      <td>1974</td>\n",
       "      <td>702.625469</td>\n",
       "      <td>0.355940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>69</td>\n",
       "      <td>12</td>\n",
       "      <td>1434</td>\n",
       "      <td>694.028269</td>\n",
       "      <td>0.483981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>71</td>\n",
       "      <td>13</td>\n",
       "      <td>3529</td>\n",
       "      <td>700.635833</td>\n",
       "      <td>0.198537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>73</td>\n",
       "      <td>12</td>\n",
       "      <td>1296</td>\n",
       "      <td>559.475436</td>\n",
       "      <td>0.431694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>73</td>\n",
       "      <td>13</td>\n",
       "      <td>3412</td>\n",
       "      <td>190.051944</td>\n",
       "      <td>0.055701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>74</td>\n",
       "      <td>12</td>\n",
       "      <td>1520</td>\n",
       "      <td>701.708422</td>\n",
       "      <td>0.461650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>75</td>\n",
       "      <td>12</td>\n",
       "      <td>2798</td>\n",
       "      <td>700.763872</td>\n",
       "      <td>0.250452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>76</td>\n",
       "      <td>13</td>\n",
       "      <td>3243</td>\n",
       "      <td>697.683611</td>\n",
       "      <td>0.215135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>78</td>\n",
       "      <td>12</td>\n",
       "      <td>100</td>\n",
       "      <td>4.079381</td>\n",
       "      <td>0.040794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>85</td>\n",
       "      <td>12</td>\n",
       "      <td>1829</td>\n",
       "      <td>701.984588</td>\n",
       "      <td>0.383808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>87</td>\n",
       "      <td>13</td>\n",
       "      <td>200</td>\n",
       "      <td>22.860833</td>\n",
       "      <td>0.114304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>87</td>\n",
       "      <td>22</td>\n",
       "      <td>416</td>\n",
       "      <td>68.342500</td>\n",
       "      <td>0.164285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>89</td>\n",
       "      <td>13</td>\n",
       "      <td>2614</td>\n",
       "      <td>696.434444</td>\n",
       "      <td>0.266425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>90</td>\n",
       "      <td>12</td>\n",
       "      <td>1115</td>\n",
       "      <td>556.965269</td>\n",
       "      <td>0.499520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>91</td>\n",
       "      <td>13</td>\n",
       "      <td>2642</td>\n",
       "      <td>679.879722</td>\n",
       "      <td>0.257335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>93</td>\n",
       "      <td>12</td>\n",
       "      <td>1300</td>\n",
       "      <td>675.491586</td>\n",
       "      <td>0.519609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>94</td>\n",
       "      <td>13</td>\n",
       "      <td>810</td>\n",
       "      <td>77.646667</td>\n",
       "      <td>0.095860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>95</td>\n",
       "      <td>12</td>\n",
       "      <td>803</td>\n",
       "      <td>482.557397</td>\n",
       "      <td>0.600943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>95</td>\n",
       "      <td>13</td>\n",
       "      <td>2094</td>\n",
       "      <td>189.791111</td>\n",
       "      <td>0.090636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>98</td>\n",
       "      <td>13</td>\n",
       "      <td>3074</td>\n",
       "      <td>696.746944</td>\n",
       "      <td>0.226658</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    操作人员 ID  工序  完成案卷的数量     总耗时 (h)  每个案卷的平均耗时 (h/卷)\n",
       "0         8  13       24    0.304444         0.012685\n",
       "1         8  15      868  598.211111         0.689183\n",
       "2         8  22       13    0.396389         0.030491\n",
       "3        10  12        9   23.514813         2.612757\n",
       "4        10  13        9   23.179722         2.575525\n",
       "5        10  22    11011  703.413333         0.063883\n",
       "6        11  12     2916  696.147973         0.238734\n",
       "7        12  12        1    0.010106         0.010106\n",
       "8        12  13        1    0.029444         0.029444\n",
       "9        12  22     8010  676.425833         0.084448\n",
       "10       13  13        1    0.184722         0.184722\n",
       "11       13  22     6574  674.174722         0.102552\n",
       "12       14  12      239  190.275164         0.796130\n",
       "13       14  13      823  401.158889         0.487435\n",
       "14       17  12       20  170.852195         8.542610\n",
       "15       17  13      245  332.440278         1.356899\n",
       "16       17  15    27102  698.116389         0.025759\n",
       "17       17  22       23  170.129167         7.396920\n",
       "18       19  13     4557  697.493056         0.153060\n",
       "19       22  12     1461  676.280517         0.462889\n",
       "20       24  12       13  170.304653        13.100358\n",
       "21       24  13      103  293.551944         2.850019\n",
       "22       24  15      698    5.671389         0.008125\n",
       "23       24  22        3    0.111389         0.037130\n",
       "24       29  12     1763  552.699996         0.313500\n",
       "25       33  12     2015  672.698110         0.333845\n",
       "26       34  15      796  533.338056         0.670023\n",
       "27       42  12     1032  406.980950         0.394361\n",
       "28       42  22     5812  316.105556         0.054388\n",
       "29       44  12     2202  701.156312         0.318418\n",
       "30       45  12     1734  701.340502         0.404464\n",
       "31       48  13     3561  713.712778         0.200425\n",
       "32       54  13      179   50.518889         0.282228\n",
       "33       62  12     3103  703.105706         0.226589\n",
       "34       68  12     1974  702.625469         0.355940\n",
       "35       69  12     1434  694.028269         0.483981\n",
       "36       71  13     3529  700.635833         0.198537\n",
       "37       73  12     1296  559.475436         0.431694\n",
       "38       73  13     3412  190.051944         0.055701\n",
       "39       74  12     1520  701.708422         0.461650\n",
       "40       75  12     2798  700.763872         0.250452\n",
       "41       76  13     3243  697.683611         0.215135\n",
       "42       78  12      100    4.079381         0.040794\n",
       "43       85  12     1829  701.984588         0.383808\n",
       "44       87  13      200   22.860833         0.114304\n",
       "45       87  22      416   68.342500         0.164285\n",
       "46       89  13     2614  696.434444         0.266425\n",
       "47       90  12     1115  556.965269         0.499520\n",
       "48       91  13     2642  679.879722         0.257335\n",
       "49       93  12     1300  675.491586         0.519609\n",
       "50       94  13      810   77.646667         0.095860\n",
       "51       95  12      803  482.557397         0.600943\n",
       "52       95  13     2094  189.791111         0.090636\n",
       "53       98  13     3074  696.746944         0.226658"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_all"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa22be7d",
   "metadata": {},
   "source": [
    "## 任务2.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "0c86bc69",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#2.1\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 设置字体\n",
    "font = {'family': 'SimSun', 'weight': 'normal', 'size': 12}\n",
    "plt.rc('font', **font)\n",
    "\n",
    "# 读取数据文件\n",
    "ddata = pd.read_excel(r'C:\\Users\\30382\\Desktop\\A题-档案数字化加工流程数据分析\\data.xlsx')\n",
    "\n",
    "# 转换时间格式\n",
    "data['dPROC_TIME'] = pd.to_datetime(data['dPROC_TIME'])\n",
    "\n",
    "# 按工序和日期分组计算案卷数量\n",
    "grouped = data.groupby(['sNODE_NAME', data['dPROC_TIME'].dt.date]).size().reset_index(name='案卷数量')\n",
    "\n",
    "# 绘制簇状柱形图\n",
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "colors = ['blue', 'green', 'orange', 'red'] # 不同工序的颜色\n",
    "for i, group in enumerate(set(grouped['sNODE_NAME'])):\n",
    "    group_data = grouped[grouped['sNODE_NAME'] == group]\n",
    "    ax.bar(group_data['dPROC_TIME'], group_data['案卷数量'], color=colors[i], label=group)\n",
    "\n",
    "# 设置图表标签\n",
    "ax.set_xlabel('时间')\n",
    "ax.set_ylabel('完成案卷数量')\n",
    "ax.set_title('每天不同工序完成案卷数量')\n",
    "ax.legend()\n",
    "\n",
    "# 调整 x 轴标签显示格式为日期\n",
    "plt.xticks(rotation=45)\n",
    "\n",
    "# 显示图表\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "efb69b78",
   "metadata": {},
   "source": [
    "## 任务2.2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "1c6ace8d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x600 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#2.2\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "\n",
    "# 将时间列转换为日期\n",
    "data['dUPDATE_TIME'] = pd.to_datetime(data['dUPDATE_TIME'])\n",
    "data['date'] = data['dUPDATE_TIME'].dt.date\n",
    "\n",
    "# 计算工作时长（单位：人·小时）\n",
    "data['工作时长'] = (data['dNODE_TIME'] - data['dUPDATE_TIME']).dt.total_seconds() / 3600\n",
    "\n",
    "# 统计每天各工序的投入工作量\n",
    "daily_work_duration_by_process = data.groupby(['date', 'iFLOW_NODE_NO'])['工作时长'].sum().unstack(fill_value=0)\n",
    "\n",
    "# 绘制多重折线图\n",
    "plt.figure(figsize=(10, 6))\n",
    "colors = ['blue', 'orange', 'green', 'red']  # 每个工序对应的颜色\n",
    "daily_work_duration_by_process.plot(kind='line', marker='o', color=colors)\n",
    "plt.title('各工序每天投入工作量')\n",
    "plt.xlabel('日期')\n",
    "plt.ylabel('投入工作量（人·小时）')\n",
    "plt.legend(title='工序', loc='upper right')\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e25530e3",
   "metadata": {},
   "source": [
    "## 任务2.3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "c2185f59",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#2.3\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# 转换时间格式\n",
    "data['dPROC_TIME'] = pd.to_datetime(data['dPROC_TIME'])\n",
    "\n",
    "# 计算每个工序每天的返工案卷数和当天返工案卷总数\n",
    "grouped = data.groupby(['sNODE_NAME', data['dPROC_TIME'].dt.date])['iNODE_STATUS'].value_counts(normalize=True).rename('percentage').reset_index()\n",
    "grouped = grouped[grouped['iNODE_STATUS'] == 5] # 只保留返工案卷\n",
    "\n",
    "# 创建堆积面积图\n",
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "colors = ['blue', 'green', 'orange', 'red'] # 不同工序的颜色\n",
    "sns.set_palette(sns.color_palette(colors))\n",
    "\n",
    "# 根据不同工序绘制堆积面积图\n",
    "for sNODE_NAME, group in grouped.groupby('sNODE_NAME'):\n",
    "    ax.fill_between(group['dPROC_TIME'], group['percentage'], label=sNODE_NAME, alpha=0.5, step='post')\n",
    "\n",
    "# 设置图表标签\n",
    "ax.set_xlabel('日期')\n",
    "ax.set_ylabel('返工案卷数占比')\n",
    "ax.set_title('每天各工序返工案卷数占比堆积面积图')\n",
    "\n",
    "# 调整 x 轴标签显示格式为日期\n",
    "plt.xticks(rotation=45)\n",
    "\n",
    "# 添加图例\n",
    "plt.legend()\n",
    "\n",
    "# 显示图表\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e587a0fe",
   "metadata": {},
   "source": [
    "## 任务2.4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "e0ba36e0",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#2.4\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 筛选图像处理工序的返工案卷\n",
    "image_processing_rework_cases = rework_cases[rework_cases['iFLOW_NODE_NO'] == 3]\n",
    "\n",
    "# 汇总每个操作人员返工案卷数\n",
    "rework_count_by_user = image_processing_rework_cases.groupby('iUSER_ID').size()\n",
    "\n",
    "# 计算每个操作人员返工案卷数占该工序返工案卷总数的百分比\n",
    "rework_percentage_by_user = (rework_count_by_user / rework_count_by_user.sum()) * 100\n",
    "\n",
    "# 按百分比进行排序\n",
    "rework_percentage_by_user = rework_percentage_by_user.sort_values(ascending=False)\n",
    "\n",
    "# 合并排名第 10 位及以后的百分比\n",
    "if len(rework_percentage_by_user) > 10:\n",
    "    rework_percentage_by_user['其他'] = rework_percentage_by_user.iloc[9:].sum()\n",
    "    rework_percentage_by_user = rework_percentage_by_user[:10]\n",
    "\n",
    "# 绘制饼图\n",
    "plt.figure(figsize=(8, 8))\n",
    "plt.pie(rework_percentage_by_user, labels=rework_percentage_by_user.index, autopct='%1.1f%%', startangle=90)\n",
    "plt.title('图像处理工序每个操作人员返工案卷数占比')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "25a513fb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "领取提交模式分析结果已保存到 result3.xlsx\n"
     ]
    }
   ],
   "source": [
    "#3\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "# 读取数据文件\n",
    "df = pd.read_excel(r'C:\\Users\\30382\\Desktop\\A题-档案数字化加工流程数据分析\\data.xlsx', sheet_name='Sheet1')\n",
    "\n",
    "# 重置索引为默认值\n",
    "df.reset_index(drop=True, inplace=True)\n",
    "\n",
    "\n",
    "\n",
    "# 替换开始时间和完成时间字段名\n",
    "df.rename(columns={'开始时间': 'dUPDATE_TIME', '完成时间': 'dNODE_TIME'}, inplace=True)\n",
    "\n",
    "# 提取起始时间和完成时间列\n",
    "time_df = df[['dUPDATE_TIME', 'dNODE_TIME']]\n",
    "\n",
    "# 计算每个案卷的领取和提交时间差\n",
    "time_diff_df = time_df.diff(axis=1)\n",
    "\n",
    "# 根据起始时间差计算开始和结束时间\n",
    "start_time_df = time_df['dUPDATE_TIME']\n",
    "end_time_df = time_df['dNODE_TIME']\n",
    "\n",
    "# 分析领取提交模式\n",
    "patterns = []\n",
    "for index, row in time_diff_df.iterrows():\n",
    "    start_time = start_time_df[index]\n",
    "    end_time = end_time_df[index]\n",
    "    if pd.isnull(start_time) or pd.isnull(end_time):\n",
    "        patterns.append('未知')\n",
    "        continue\n",
    "    if row['dUPDATE_TIME'] > pd.Timedelta(hours=1) or row['dNODE_TIME'] > pd.Timedelta(hours=1):\n",
    "        patterns.append('分多次领取、分多次提交')\n",
    "    elif row['dUPDATE_TIME'] > pd.Timedelta(hours=1):\n",
    "        patterns.append('分多次领取、连续提交')\n",
    "    elif row['dNODE_TIME'] > pd.Timedelta(hours=1):\n",
    "        patterns.append('连续领取、分多次提交')\n",
    "    else:\n",
    "        patterns.append('连续领取、连续提交')\n",
    "\n",
    "# 构建结果表格\n",
    "result_df = pd.DataFrame({'案卷号': df['sARCH_ID'], '领取提交模式': patterns})\n",
    "\n",
    "# 保存结果到文件\n",
    "result_df.to_excel(r'C:\\Users\\30382\\Desktop\\A题-档案数字化加工流程数据分析\\result\\result3.xlsx', index=False)\n",
    "\n",
    "print('领取提交模式分析结果已保存到 result3.xlsx')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98710843",
   "metadata": {},
   "source": [
    "## 任务3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "d910e498",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>案卷号</th>\n",
       "      <th>领取提交模式</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>托644031-册一</td>\n",
       "      <td>连续领取、连续提交</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>托644032-册一</td>\n",
       "      <td>连续领取、连续提交</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>托7181-册一</td>\n",
       "      <td>连续领取、连续提交</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>托7182-册一</td>\n",
       "      <td>连续领取、连续提交</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>托7183-册一</td>\n",
       "      <td>连续领取、连续提交</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135935</th>\n",
       "      <td>托751498-册一</td>\n",
       "      <td>未知</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135936</th>\n",
       "      <td>托751499-册一</td>\n",
       "      <td>未知</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135937</th>\n",
       "      <td>托751500-册一</td>\n",
       "      <td>未知</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135938</th>\n",
       "      <td>托751456之一-册一</td>\n",
       "      <td>未知</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135939</th>\n",
       "      <td>托694750-册一</td>\n",
       "      <td>未知</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>135940 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 案卷号     领取提交模式\n",
       "0         托644031-册一  连续领取、连续提交\n",
       "1         托644032-册一  连续领取、连续提交\n",
       "2           托7181-册一  连续领取、连续提交\n",
       "3           托7182-册一  连续领取、连续提交\n",
       "4           托7183-册一  连续领取、连续提交\n",
       "...              ...        ...\n",
       "135935    托751498-册一         未知\n",
       "135936    托751499-册一         未知\n",
       "135937    托751500-册一         未知\n",
       "135938  托751456之一-册一         未知\n",
       "135939    托694750-册一         未知\n",
       "\n",
       "[135940 rows x 2 columns]"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "id": "1992b442",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "领取提交模式分析结果已保存到 result3.xlsx\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# 读取数据文件\n",
    "df = pd.read_excel(r'C:\\Users\\30382\\Desktop\\A题-档案数字化加工流程数据分析\\data.xlsx', sheet_name='Sheet1')\n",
    "\n",
    "# 重置索引为默认值\n",
    "df.reset_index(drop=True, inplace=True)\n",
    "\n",
    "# 替换开始时间和完成时间字段名\n",
    "df.rename(columns={'开始时间': 'dUPDATE_TIME', '完成时间': 'dNODE_TIME'}, inplace=True)\n",
    "\n",
    "# 提取起始时间和完成时间列\n",
    "time_df = df[['dUPDATE_TIME', 'dNODE_TIME']]\n",
    "\n",
    "# 计算每个案卷的领取和提交时间差\n",
    "time_diff_df = time_df.diff(axis=1)\n",
    "\n",
    "# 根据起始时间差计算开始和结束时间\n",
    "start_time_df = time_df['dUPDATE_TIME']\n",
    "end_time_df = time_df['dNODE_TIME']\n",
    "\n",
    "# 分析领取提交模式\n",
    "patterns = []\n",
    "for index, row in time_diff_df.iterrows():\n",
    "    start_time = start_time_df[index]\n",
    "    end_time = end_time_df[index]\n",
    "    if pd.isnull(start_time) or pd.isnull(end_time):\n",
    "        patterns.append('未知')\n",
    "        continue\n",
    "    if row['dUPDATE_TIME'] > pd.Timedelta(hours=1) or row['dNODE_TIME'] > pd.Timedelta(hours=1):\n",
    "        patterns.append('分多次领取、分多次提交')\n",
    "    elif row['dUPDATE_TIME'] > pd.Timedelta(hours=1):\n",
    "        patterns.append('分多次领取、连续提交')\n",
    "    elif row['dNODE_TIME'] > pd.Timedelta(hours=1):\n",
    "        patterns.append('连续领取、分多次提交')\n",
    "    else:\n",
    "        patterns.append('连续领取、连续提交')\n",
    "\n",
    "# 按操作人员ID从小到大排序\n",
    "df.sort_values('iUSER_ID', inplace=True)\n",
    "\n",
    "# 构建结果表格\n",
    "result_df = pd.DataFrame({\n",
    "    '模式序号': [1] * len(df),\n",
    "    '操作人员ID': df['iUSER_ID'], \n",
    "    '批号': df['sBatch_number'], \n",
    "    '时间': df['dUPDATE_TIME'],  \n",
    "    '操作': patterns,\n",
    "    '案卷号': df['sARCH_ID']\n",
    "})\n",
    "\n",
    "# 保存结果到文件\n",
    "result_df.to_excel(r'C:\\Users\\30382\\Desktop\\A题-档案数字化加工流程数据分析\\result\\result3.xlsx', index=False)\n",
    "\n",
    "print('领取提交模式分析结果已保存到 result3.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "id": "e792427f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>模式序号</th>\n",
       "      <th>操作人员ID</th>\n",
       "      <th>批号</th>\n",
       "      <th>时间</th>\n",
       "      <th>操作</th>\n",
       "      <th>案卷号</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>43176</th>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>2020-07-14 17:04:44</td>\n",
       "      <td>连续领取、连续提交</td>\n",
       "      <td>托677038-册一</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43500</th>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>2020-07-14 17:04:10</td>\n",
       "      <td>连续领取、连续提交</td>\n",
       "      <td>托679465-册一</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43501</th>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>2020-07-14 17:04:10</td>\n",
       "      <td>连续领取、连续提交</td>\n",
       "      <td>托679466-册一</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43502</th>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>2020-07-14 17:04:10</td>\n",
       "      <td>连续领取、连续提交</td>\n",
       "      <td>托679467-册一</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43503</th>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>2020-07-14 17:04:10</td>\n",
       "      <td>连续领取、连续提交</td>\n",
       "      <td>托679468-册一</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44347</th>\n",
       "      <td>1</td>\n",
       "      <td>98</td>\n",
       "      <td>1612</td>\n",
       "      <td>2020-07-14 14:38:03</td>\n",
       "      <td>未知</td>\n",
       "      <td>托680283-册一</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4754</th>\n",
       "      <td>1</td>\n",
       "      <td>98</td>\n",
       "      <td>1596</td>\n",
       "      <td>2020-07-03 12:01:59</td>\n",
       "      <td>未知</td>\n",
       "      <td>托671943-册一</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44345</th>\n",
       "      <td>1</td>\n",
       "      <td>98</td>\n",
       "      <td>1612</td>\n",
       "      <td>2020-07-14 14:38:03</td>\n",
       "      <td>未知</td>\n",
       "      <td>托680282-册一</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31022</th>\n",
       "      <td>1</td>\n",
       "      <td>98</td>\n",
       "      <td>1608</td>\n",
       "      <td>2020-07-10 16:31:51</td>\n",
       "      <td>未知</td>\n",
       "      <td>托638381-册一</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59062</th>\n",
       "      <td>1</td>\n",
       "      <td>98</td>\n",
       "      <td>1616</td>\n",
       "      <td>2020-07-17 11:35:39</td>\n",
       "      <td>未知</td>\n",
       "      <td>托680167-册一</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>135940 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       模式序号  操作人员ID    批号                  时间         操作         案卷号\n",
       "43176     1       8     1 2020-07-14 17:04:44  连续领取、连续提交  托677038-册一\n",
       "43500     1       8     1 2020-07-14 17:04:10  连续领取、连续提交  托679465-册一\n",
       "43501     1       8     1 2020-07-14 17:04:10  连续领取、连续提交  托679466-册一\n",
       "43502     1       8     1 2020-07-14 17:04:10  连续领取、连续提交  托679467-册一\n",
       "43503     1       8     1 2020-07-14 17:04:10  连续领取、连续提交  托679468-册一\n",
       "...     ...     ...   ...                 ...        ...         ...\n",
       "44347     1      98  1612 2020-07-14 14:38:03         未知  托680283-册一\n",
       "4754      1      98  1596 2020-07-03 12:01:59         未知  托671943-册一\n",
       "44345     1      98  1612 2020-07-14 14:38:03         未知  托680282-册一\n",
       "31022     1      98  1608 2020-07-10 16:31:51         未知  托638381-册一\n",
       "59062     1      98  1616 2020-07-17 11:35:39         未知  托680167-册一\n",
       "\n",
       "[135940 rows x 6 columns]"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_df"
   ]
  }
 ],
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